Comprehensive GIS-Based Solution for Road Blockage Due to Seismic Building Collapse in Tehran

Abstract eng:
Tehran is highly vulnerable to large earthquakes. Known active faults are surrounding the city and the Ray fault, in south of Tehran, in west-east direction, has high potentials to devastate the city. The municipality district 17 of Tehran is the area of interest for this research as it is considered a seismic hard hit zone comprising of extremely vulnerable dwellings. It is of crucial importance to estimate the road network performance after the occurrence of a disastrous earthquake in urban areas. A proper disaster management scheme requires that such studies be completed ahead of time before a devastating event to have a thorough understanding of the situation and to plan especially for emergency activities (search/rescue, safe evacuation, medical aid, etc…). This paper describes our comprehensive GIS-based methodology for modeling and estimating the severity and the spatial distribution of road blockage as probability measures, for each road segment (between consecutive intersections) and for the given scenario earthquake. The methodology has four major steps namely seismic hazard identification, building inventory and road network geodatabase development, building vulnerability functions assignment followed by identification of building collapse, and finally producing a risk map for road blockage by a simplified computed index. The seismic hazard is the estimated worst case scenario from the Ray fault model (JICA 2000). The building inventory has been produced using aerial remote sensing cartographic data that reflect parcel information and attributes from different field surveys. The road network has been extracted from our semi-automated algorithm using 1:2000 scale city maps. Some structural vulnerability functions for various structural typologies have been chosen and reported by JICA (2000) for Tehran. We have found and implemented essential corrections to these functions and used for our building loss estimation model. Ultimately, we introduce a method to report the severity and the spatial location of the blocked roads within the network.

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